Production level output response | Management homework help

Requesting 200 words response to the following post using at least three substantive peer-reviewed scholarly journal articles (different than in the below post) to provide those substantive replies.   You may utilize only the main article as a reference.
Production Level Output
Key Term 
Understanding the key contributing factors effecting production level output is a key ingredient to a successful enterprise. Consider a window manufacturing company as an example. It is necessary to understand how many windows can be made based upon consumer needs to maximize production supplies, capabilities, and distribution. Without knowing how many windows can be built, given the current size of the operation, and costs associated with building them, there is no guarantee of future profitability. This topic is important on many focusing on the bigger picture of operational planning and strategy. Satterlee, (2018) discusses the major impact operational managers have on all areas directly related to the success or failure of a firm. 
Explanation of Key Term
Production level output is a primary consideration for any organization within the production arena. The careful planning of production levels increases in importance when dealing with a global operation. According to Satterlee, (2018) capacity, how much product to produce or how many customers serviced, and process, how goods or services are made and delivered, are the two key areas of concern. Much of a production-oriented business’s capacity to produce their respective product is directly related to the amount of raw materials available and the physical size of the overall operation. The process side of the house is concerned with creation, marketing, inventory, quality, and delivery (Satterlee, 2018). Production level output is a multi-faceted and complex portion of conducting business on a global stage.
Major Article Summary 
The major article by Hafezalkotob et al., (2019) directly relates to planning issues regarding production level output by offering a cooperative aggregate production solution for manufacturing firms with multiple facilities. In their study, the total costs of production, including workforce and inventory, are considered to be areas where costs may be reduced as demand increases. The game theory approach is applied successfully throughout the body of their research. 
Hafezalkotob et al., (2019) takes into consideration the minimization of costs, inventory levels, human capital, wages, production rates, and plant operations constructing an aggregating production plan (APP). When multiple producers of different products work in cooperation of one another, this study shows a distinct benefit. Moreover, they cite multiple beneficial reasons production plants work together to include but not limited to conventions, globalization, and legislation. These partnerships can ultimately reduce production costs which can increase profit margins. Additional benefits were realized in their research relating to an increase in worker job security and skill allowing for lean production to occur.
The article concludes with accurate analysis of data and recommendations for further research. Hafezalkotob et al., (2019) were able to quantify “the cost-saving opportunity of the cooperation of plants caused by decreases in inventory and workforce levels”. The affected production plans realize significant cost reduction and higher satisfaction resulting in higher profitability. This is accomplished through full interchangeability of employees and inventory among cooperating participants. 
Because this article discusses production planning on a global scale, it directly targets the planning issues discussed by Satterlee, (2018). When capacities manufacturing plants work together and share the burden of inventory and workforce, there is a real cost saving effect. The managers have an ability to tailor their approach and delivery of products in the most efficient manner, therefore maximizing profits. Standardization practices across a group of cooperating firms can ensure uniformity which adds to additional savings (Satterlee, 2018). Sharing the burden of production remains the most advantageous approach for a large number of manufacturing companies across the globe. 
The best way to describe the relationship between the article by Hafezalkotob et al., (2019) and global business is that the article expounds upon the topic of planning issues through a decentralized approach to production level output. In contrast to a more traditional one dog, one bone approach, sharing the burden of production with more than one plant can alleviate location or resource factors affecting output. When approached in a collaborative manner, cost savings will accumulate allowing managers flexibility to consider necessary changes to key processes or facilitate relationships with future localities. 
If cooperative aggregate production planning is conducted properly, it could also answer the make or buy decision formulated by Satterlee, (2018). For example, if two production plants are working together, it may be more efficient for one plant to produce items necessary to supply the other alleviating the need to outsource at a higher cost. At times, suppliers may increase prices based upon supply and demand causing profit margins to decrease, however, if that was no longer a factor, the cost of production could be steady and manageable. 
The four alternative works cited directly relate to the issue of production level output. Chand et al., (2018) focuses their work on multiple production lines and process design for volume flexibility. In their article, they research companies that produce one product with many machines or lines and how to best maximize production rates in the face of varying demands. 
Yuanbin et. al., (2019) researches a cloud-based approach for 3D printing firms. This is a direct reflection of how technological advancements effect production planning. With 3D printing changing the availability of parts or materials, lower manufacturing costs may be realized allowing for either greater profit margins, or lower costs to the customer. They find quality can remain high and speed of delivery can be increased through the use of cloud-based ordering. These issues are directly related to production planning.
The article by Freitag et al., (2019) follows suit with an evaluation of the “digitalization of production” and the effect it has on product life cycles and lead times. They show how fluctuations in demand can make production planning difficult and reduce the monetary constraints of up-front investments. On-demand production capacities have a direct impact on how managers predict consumer needs and how best to approach production capacity limitations. 
Lastly, Djordjevic et al., (2019) utilizes the automotive industry to discuss aggregate production planning (APP). They show that although majority of the planning processes may be “deterministic in nature”, there are still ambiguities managers must be able to mitigate. They conclude finding their model has the ability to improve the performance of automobile manufactures “measured by time”. These are all important factors for managers to consider throughout the planning process.
Chand, S., Teyarachakul Prime, S., & Sethi, S. (2018). Production planning with multiple production lines: Forward algorithm and insights on process design for volume flexibility. Naval Research Logistics, 65(6-7), 535-549.!/search?bookMark=eNp1kEtLxDAUhYMoOD4W_oNsXXRM0uZRd4P4gkFdqLgraXM7RtJkSDqOg3_e6Aiu5C4uHL5zFt8B2vXBA0InlEwpIezM6_cpo4rKHTShnJFCSE520YSouiqIqF_20UFKb4QQURE-QZ8PMZhVN9rg8dJp761f4LUdX_GwcqNdOsDLP8JZD-kcX4W41tFg7RYhZnbA2htsfbKL1zHh76kYOkgJG8iZx32I-D241QC4d_BhW-vsuDlCe712CY5__yF6urp8vLgp5vfXtxezedExwWRhWtHKmpJSCS4Fp6aktYLSGMYUMxKAcapKUzFdV0pLqXpl-hKohLblQHh5iKbb3YV20FjfhzHqLp-BwXbZX29zPuNCKFbViubC6bbQxZBShL5ZRjvouGkoab41N1lz86M5s2dbdp1HNv-Dzd3sedv4ApUDg10
Djordjevic, I., Petrovic, D., & Stojic, G. (2019). A fuzzy linear programming model for aggregated production planning (APP) in the automotive industry. Computers in Industry, 110, 48-63.!/search?bookMark=eNqFkD1rwzAQhkVJoUnan9CisR3sSrYlO1MxoV8QaIZ2FrJ0dhVsychOIPn1VUiGDoXeDQfH8x7cM0MT6ywgdEtJTAnlj5tYua43VscJoYuYsJiQ7AJNaZEnEaeLbIKmgeNRyim7QrNh2JBQec6nSJe43h4Oe9waC9Lj3rvGy64ztsGd09Di2nksm8ZDI0fQR0Bv1WicxX0rrT2C9-V6_YCNxeM3YLkdXedGs4Ow0dth9PtrdFnLdoCb85yjr5fnz-VbtPp4fV-Wq0glecKiigFN8koBqRcVD4MXCrIKgDJe6bTOuQamEghMmss8owFIq0prqqqMyiydo_h0t5EtCGNrN3qpQmvojArSahP2JSuKImWM0hC4-yMgfgPsBCjvhsFDLXpvOun3ghJxlC824ixfHOULwkSQH3JPpxyEd3cGvBiUAatAGw9qFNqZfy78AMmFkpg
Freitag, B., Häfner, L., Pfeuffer, V., & Übelhör, J. (2020). Evaluating investments in flexible on-demand production capacity: A real options approach. Business Research (Göttingen), 13(1), 133-161.!/search?bookMark=eNotkE1Lw0AQhhepYK39A54WPEdnNtmPepNSP6DgRQ-elkmy0Ui6idmktf_ere1pXoaHeZnnkk186x1j1wi3CKDvQgbKyARwkQAgyGR3xqYCFyYRSujJKacZiAs2D6HOQahUg5Byyj5WW2pGGmr_yWu_dWHYOD-EmHnVuN86bxxvfVK6DfmSd31bjsVQt54X1FFRD_t7Trx31PC2O-wDpy5SVHxdsfOKmuDmpzlj74-rt-Vzsn59elk-rBMUUmMiASutgUQuVWZ0TiUschTKlA5TAYXJEJ2pqFwQSC0rMkoVsoqQSk2OZTpjN8e7sfZnjA_Y73bsfay0IjVaSokZRkocqaJvQ-hdZbu-3lC_twj2YNEeLdpo0f5btLv0D8IHZyA
Hafezalkotob, A., Chaharbaghi, S., & Lakeh, T. M. (2019). Cooperative aggregate production planning: A game theory approach. Journal of Industrial Engineering International, 15(S1), 19-37.!/search?bookMark=eNo9kE1rwzAMhs3oYF3XH7BbYOdskj_idrdS9lEo7LLBbkZx7DSljTMnHfTfz13HLCyBeHklPdds1IbWMXaLcI8A-qGXAHOeA85zECByuGBjzhXmGvnniI1RC5UrDfyKTft-C-lpPQdRjNliGULnIg3Nt8uorqOraXBZF0N1sEMT2qzbUds2bf2YUVbT3mXDxoV4zKhLIrKbG3bpade76V-dsI_np_fla75-e1ktF-scRYq8KlBrtCS8AlGCK63SvoBCaqQCK2UBBIcClJUgqPQSCEvpS2vTd6UXE7Y6-1aBtqaLzZ7i0QRqzG8jxNpQHBq7c6ZCzyupyaMQUiKUqErvlbdy5lHZk9fd2Sud8HVw_WC24RDbtL7hqGdSFXw2Tyo8q2wMfR-d_5-KYE7gzRm8SeDNCXxKP27Iduo
Satterlee, B. C. (2018). In Cross border commerce: with Biblical worldview application (3rd ed., pp. 44–46). essay, SI-CORP.
Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing—A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58, 145-157.!/search?bookMark=eNp9kMFq3DAQhkVIIJu0L5CTX8DuyLIsG3pZQtMEAu2hPRYhj0aJlrVkZHshtzxEnjBPUi2bXsscBob5fma-K3YeYiDGbjhUHHj7ZVcl9GNVA-8rEBWAOGMb3qm-rKVQ52wDSrSl7Bp5ya7meQcAdSPFhv35maJdcfExFNPehODDU-FiKnAfV1sOZiZbGGv94g9UjCaszuCyprz2_vq2LTCO07pQKg5-zhn_gGlK0eDzJ3bhzH6mzx_9mv2–_br9r58_PH94Xb7WGItJC-V7PpB9QOKplVNPswOvTUcFKLjijeDEgCWcyIgrG3nSGILbe2UBdFJJa5Zdcp9MnvSPri4JIO5LI0esyrn83wrO-i6WvIjUJ8ATHGeEzk9JT-a9KI56KNRvdNHo_poVIPQ2WiGvp4gyq8cPCU9o6eAZH0iXLSN_n_4X1bCgvk     


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